(Katholieke Universiteit Leuven), Market Structure and Innovation

39 Pages·2009·0.49 MB·English

by  Federal Trade Commission

Market Structure and Innovation:
A Dynamic Analysis of the Global Automobile Industry∗
Aamir Rafique Hashmi†
and
Johannes Van Biesebroeck‡
January 20, 2009
Abstract
Westudytherelationshipbetweenmarketstructureandinnovationintheglobalautomobile
industryforthe1982-2004period. WeusethedynamicindustryframeworkofEricsonandPakes
[1995]andestimatetheparametersofthemodelusingatwo-stepprocedureproposedbyBajari
et al. [2007]. Since the industry has seen a lot of consolidation since 1982, mergers are an
important ingredient of our model.
After estimating the parameters of the model, we simulate the industry forward and study
howchangingmarketstructure(mainlyduetomergers)affectsinnovativeactivityatthefirmas
well as at the industry level. Our findings are the following: (1) The effect of market structure
on innovation in the global auto industry depends on the initial state. If the industry is not
very concentrated, as it was in 1982, some consolidation may increase the innovative activity.
However, if the industry is already concentrated, as in 2004, further consolidation may reduce
the incentives to innovate. (2) Mergers reduce the value of merging firms though they may
increase the aggregate value of the industry.
Key words: Competition and Innovation; Automobile Industry; Dynamic Games
JEL Classification Codes: C73; L13; L62; O31
∗WethankparticipantsattheconferencesoftheCEA(MontrealandHalifax),EEA(Vienna),IIOC(Savannah),
SED(Prague),andattheseminarsattheUniversityofToronto,NationalUniversityofSingapore,VictoriaUniversity
(Wellington,NewZealand)andLondonSchoolofEconomicsfortheircomments. WealsothanksBartVanLooyand
Xiaoyan Song for help with patent data. Financial support from AUTO21, CFI, and SSHRC is greatly appreciated.
†Department of Economics, National University of Singapore. E-mail: [email protected]
‡Department of Economics, University of Toronto and NBER. E-mail: [email protected]
1
1 Introduction
Inthispaper, weinvestigatehowchangesinindustrystructureinteractwiththeinnovativeactivity
of the incumbent firms in the global automobile industry. The question of interaction between
market structure and innovation has been the subject of intense research since Schumpeter [1950].1
However, there is hardly any study on the global automobile industry. This is surprising given the
fact that the global automobile industry has not only undergone interesting changes in its market
structure in recent years, but is also one of the most innovative industries in the global economy.
The global automobile industry has seen significant consolidation over the last few decades.
Many of the industry giants have found it beneficial to join hands with some of their former rivals.
The mergers between Daimler-Benz and Chrysler and between Hyundai and Kia, the association
between Renault and Nissan and the takeover of Mazda, Jaguar and Volvo by Ford are but a
few examples of this consolidation. On the one hand, this consolidation is the result of increased
competition, which has made it harder for smaller firms to survive on their own. On the other
hand, consolidation intensifies competition as the emerging groups are highly research intensive.
The top 13 firms in the auto industry spent more than 55 billion dollars on R&D in 2005 and have
obtained more than 50,000 patents from the US patent office between 1980 and 2004.
Producingautomobilesisahighlyresearchintensiveactivity: in2003,morethan13%ofallR&D
in the OECD was spent in ISIC industry 34 ‘Motor Vehicles’, more than in any other industry.
Statistics in Table 1 illustrate the comparative importance of R&D in the automotive industry
for the OECD and the three largest economic blocs. The industry is concentrated worldwide,
guaranteeing that the main firms will take actions of competitors into account when deciding on
their own innovative activities. In 2005, more than 85% of all vehicles were produced by the 13
largest firms, which are active in all major regions of the world.2
[Table 1 approximately here]
At the most fundamental level, two features are necessary for innovation to take place. First,
firms need to be able to finance innovation, i.e. there needs to be a margin between price and
marginal cost. Second, firms need to have an incentive to innovate, i.e. innovation has to increase
expectedprofits. Bothconditionsareclearlysatisfiedintheautomobileindustry. Demandestimates
for this industry—see for example Berry et al. [1995] or Goldberg [1995]—reveal that markups over
marginal costs tend to be large, consistent with the view that fixed costs are important in this
1Kamien and Schwartz [1982], Cohen and Levin [1989], Ahn [2002] and Gilbert [2006] are surveys of the relevant
literature.
2Throughout,wedonotdistinguishbetweenminorityshareholdingsandoutrightcontrol. E.g.Mazdaiscounted
as part of the Ford group, even though Ford Motor Co. never held more than 33.4% of Mazda’s shares (achieved by
1996).
2
industry. Innovation is also likely to advance a firm’s competitive position through higher product
quality and reliability, the introduction of desirable features in its products, or lower production
cost. Alargenumberofanalystsmakesalivingmeasuringthesebenefitsforconsumersandinvestors
alike.3
Unlike most of the studies that use reduced form regressions to study the relationship between
market structure and innovation, we do so in a strategic and dynamic environment. There are at
least two reasons for this. First, innovation is inherently a dynamic activity. Firms make R&D
investments today expecting uncertain future rewards through better products or more efficient
production. The magnitude of these benefits are intricately linked to the future market structure
andthelevelofinnovationofcompetitors.4 Mergingorformingstrategicallianceswithrivalsisalso
bestanalyzedinadynamicframework. Mergersinteractwithinnovationthroughtheirinfluenceon
market structure as well as through the consolidation of the knowledge stock in the industry. The
global automobile industry seems an ideal place to study these two forces and their interaction.
Second, we now have computationally tractable methods to estimate models of dynamic com-
petition. In this study, we employ a recently developed technique in the estimation of dynamic
games that does not require one to solve for the equilibrium of the game, see Bajari et al. [2007].
Our study is one of the first to put these new techniques to a practical test, demonstrate their
usefulness and highlight some practical difficulties associated with their application.5
Theprincipalobjectiveofourpaperistostudytheresponseoffirmsintermsoftheirinnovative
activity to changes in the level of competition — a combination of market structure and the level of
innovation of all market participants. We do so in a dynamic environment in which firms produce
differentiated products that differ in quality. Firms invest in R&D to improve the quality of their
products and to lower the cost of production. The market share of a firm depends on the relative
quality of its product and the price, which is set strategically in each period. Investments in R&D
increase the technological knowledge of the firm but the exact outcome of R&D is uncertain. On
average, higher knowledge translates into a higher quality product and lower marginal cost. The
investment inR&D is modeled as a strategic decision: a firm takes the actions of its rivals and their
possible future states into account before making its R&D decision. In addition, firms incorporate
potential future mergers into their expectations.
3To name but a few, J.D. Power and Consumer Reports measure defect rates in vehicles; numerous consumer
magazines and internet sites compare the performance and discuss the features of vehicles; and Harbour Consulting
and KPMG continuously compare productivity in the industry.
4Aghion and Griffith [2005] provide an overview of the issues involved and review some popular modeling ap-
proaches.
5Among a growing list of (working) papers using various approaches, we can point the interested reader to the
followingapplications: Ryan[2006],studyingtheeffectofenvironmentalregulationonthecementindustry;Collard-
Wexler [2005], studying the effect of demand fluctuation in the ready-mix concrete industry; Aguirregabiria and Ho
[2006], studying the airline industry; and Sweeting [2006] estimating switching costs for radio station formats.
3
The paper is organized as follows. The supply and demand side of the model as well as the
Markov perfect equilibrium concept we rely upon are introduced in Section 2. Section 3 introduces
the data. The two-step estimation methodology and the coefficient estimates are discussed in
Section 4. In the first step, we estimate all the static parameters and generate the value functions
usingforwardsimulation. Inthesecondstep,weestimatetheonlydynamicparameterofthemodel.
The impact of changes in market structure on incentives for innovation, firm value and consumer
utility is in Section 5 and Section 6 concludes.
2 The Model
Our modeling strategy follows Ericson and Pakes [1995]. There are n firms, each producing a dif-
ferentiated vehicle. Firms differ in their technological knowledge, which is observed by all market
participants as well as by the econometrician, and in a firm-specific quality index, known to the
market participants but not observed by the econometrician. We denote the technological knowl-
edge of firm j (j = 1,2,...,n) by ω ∈ R+ and the quality index by ξ ∈ R. For the industry
j j
as a whole, the vectors containing ω and ξ are denoted by s and s . For later use, we define
ω ξ
the vectors of ω and ξ excluding the firm j as s −j and s −j. We also define s = {s ,s ,m} and
ω ξ ω ξ
s−j = {s −j,s −j}.6
ω ξ
Time is discrete. At the beginning of each period, firms observe s and make their pricing and
investmentdecisions(seebelow). Althoughthepricingdecisionsarestatic,theinvestmentdecisions
are dynamic and depend on the current as well as expected future states of the industry.
Firms invest to increase their technological knowledge. A higher level of knowledge may boost
demand, for example, by improving vehicle quality or by introducing more innovative product
features. It may also reduce marginal cost by improving the efficiency of production. Hence, the
model features both product and process innovation. The effect of R&D investment on knowledge
is the sum of a deterministic and a random component, capturing that innovation is a stochastic
process. The technological knowledge depreciates at an exogenous rate.
There is no entry or exit in our model, reflective of the evolution of the industry over the
last twenty five years or so. However, a firm may ‘exit’ by merging with another firm. Mergers
are an important component of our model as they lead to discrete changes in market structure
and force the firms to readjust their prices and investments to take new industry structure into
account. In our model, mergers take place for exogenous reasons, but firms take them into account
when forming expectations over future valuations. In the remainder of this section we describe
the demand and supply sides in some detail and then define the Markov perfect equilibrium of the
model.
6m is the number of consumers in the market.
4
2.1 The Demand Side
Following Berry et al. [1995] and several others studying the automobile industry, we use a discrete
choice model of individual consumer behavior to model the demand side. There are n firms in
the industry producing differentiated vehicles. Vehicles differ in quality, which has two compo-
nents. The first component, observable to the market participants as well as the econometrician,
is positively related to the firm’s technological knowledge, g(ω), with ∂g(ω)/∂ω ≥ 0. The second
component (ξ) is unobservable to the econometrician, but firms and consumers know it and use it
in their pricing and purchase decisions.
There are m consumers in the market in each period and each of them buys one vehicle. m
grows at a constant rate that is exogenously given. The utility of a consumer depends on the
quality of a vehicle, its price and the consumer’s idiosyncratic preferences. The utility consumer i
gets from buying vehicle j is
u = θ g(ω )+θ log(p )+ξ +ν , i = 1,...,m, j = 1,...,n. (1)
ij ω j p j j ij
We assume that the observable vehicle quality equals log(ω +1) and ξ is the unobservable quality
j j
(to the econometrician). p is the price of the vehicle produced by firm j and is adjusted for vehicle
j
characteristics such as size and performance characteristics. θ and θ are preference parameters.
ω p
θ shows how quality conscious the consumers are and θ is a measure of their price elasticity. ν
ω p ij
is the idiosyncratic utility that consumer i gets from good j. Assuming it is i.i.d. extreme value
distributed, gives the following expected market share for firm j:
exp(θ log(ω +1)+θ log(p )+ξ )
σ (ω ,ξ ,p ,s−j,p−j) = ω j p j j , (2)
j j j j n
(cid:88)
exp(θ log(ω +1)+θ log(p )+ξ )
ω k p k k
k=1
where p is the price charged by firm j and p−j is the price vector of all the other firms (excluding
j
firm j) in the industry. The expected demand for vehicle j is simply mσ (·). Each firm’s demand
j
depends on the full price vector in the industry, directly through the denominator of (2) and
indirectly through its own price because its equilibrium price is a function of its rivals’ prices.
2.2 The Supply Side
We begin our explanation of the supply side with the period profit function. We assume that R&D
investments only generate useful knowledge with a one period lag and that prices can be adjusted
flexibly period by period. Hence, at the beginning of each period, after observing their individual
and industry states, the firms engage in a differentiated products Bertrand-Nash game. Each firm
chooses its own price to maximize profits, taking the prices of its rivals (p−j) and industry state
(s) as given.
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The profit maximization problem of an individual firm j is
π (ω ,ξ ,s−j,p−j,m) = max{(p −mc (ω ))mσ (·)−fc }, (3)
j j j j j j j j
pj
where mc is the marginal cost incurred by firm j to produce a vehicle. The marginal cost is a
j
function of the firm’s knowledge, capturing cost reducing process innovations. fc is the fixed cost
j
of operations faced by firm j. For now, the fixed cost does not play any role.
The first order condition for firm j, after some simplification, is
(p −mc (ω ))[1−σ (·)]θ +p = 0. (4)
j j j j p j
Since there are n firms, we have to solve n such first order conditions simultaneously to obtain the
equilibrium price vector p∗.7 Equilibrium profits are given by
π (ω ,ξ ,s−j,m) = (p∗−mc (ω ))mσ (ω ,ξ ,p∗,s−j,p∗−j). (5)
j j j j j j j j j j
Once we know the functional form and parameter values of the demand and cost functions, we can
evaluate (5) for any industry state s.
The investment in R&D is a strategic and dynamic decision. Each period, firms choose their
R&D investment based on the expected value of future profit streams. The problem is recursive
and can be described by the following Bellman equation
V (ω ,ξ ,s−j,m) = max{π (ω ,ξ ,s−j,m)−cx +βEV (ω(cid:48),ξ(cid:48),s(cid:48)−j,m(cid:48))}, (6)
j j j j j j j j j j
xj∈R+
where β is the discount factor, c is the R&D cost per unit of new technological knowledge and x
j
is the addition to new knowledge. A prime on a variable denotes its next period value. c is the
only dynamic parameter in our model that we estimate. The solution to (6) is a policy function
x (ω ,ξ ,s−j,m).
j j j
The knowledge stock of the firm evolves as follows:
ω(cid:48) = (1−δ)ω +x(ω ,ξ ,s−j,m)+(cid:15)ω, (7)
j j j j j
whereδ isthedepreciationrateoftechnologicalknowledgeandisexogenouslygiven. (cid:15)ω isarandom
shock that represents the uncertainty involved in doing R&D. A firm that spends cx on R&D will,
on the average, increase its knowledge stock by x. We assume that (cid:15)ω follows a well defined and
known distribution.8
7The existence and uniqueness of equilibrium in this context has been proved by Caplin and Nalebuff [1991].
8This law of motion for the state variable is less general than the one in the main analysis in Ericson and Pakes
[1995], as depreciation is deterministic, but more general than the example they analyze in detail, as we allow x to
take on a continuum of values.
6
We assume an exogenous AR(1) process for the evolution of the unobserved component of
quality (ξ).
ξ(cid:48) −ξ¯ = ρ(ξ −ξ¯)+(cid:15)ξ, (8)
j j j j j
where ξ¯ is the average value of ξ over the sample period.
j j
We assume that m follows a linear trend, which we estimate from the data.
2.3 Mergers
Given the recent evolution of the global automotive industry, there is no need to consider entry or
exit, but we do need to deal with mergers. During the twenty three year period we study (1982–
2004), a total of ten mergers, acquisitions and associations took place among the twenty three
initial firms in our sample. These were: Alfa Romeo-Fiat (1987); Ford-Jaguar (1989); GM-Saab
(1990); Ford-Volvo (1998); DaimlerBenz-Chrysler (1998); Hyundai-Kia (1998); Toyota-Daihatsu
(1999); Renault-Nissan (1999); Ford-Rover (2000) and GM-Daewoo (2001). Table 2 indicates how
these events have reshaped the structure of the industry.9
There is no clear pattern in the merger activity, making it nearly impossible to predict. Some-
times a merger is forced on a firm, as happened to Hyundai when the Korean government wanted it
to bail out Kia after the Asian economic crisis in 1998. In several instances, a larger firm has taken
over a smaller one, not unexpected in an industry with large scale economies. At the same time,
several smaller companies are thriving in spite of incessant merger predictions. Honda, BMW, and
Porsche are consistently among the most profitable firms in the industry. There have also been
instances of two large firms merging or joining forces in one form or another. The merger between
Chrysler and Daimler-Benz is the most recent such example and the association between Nissan
and Renault is another.
It is especially difficult to introduce mergers in our simple model with only two firm-level state
variables. The benefits of a higher level of knowledge—product and process innovations that boost
demandandreducecosts—canbecapturedinstantaneously. Thisprecludesasamotiveformerging
the combination of a large firm that has many models and dealerships with a smaller firm that has
a higher level of technological knowledge. Given that the policy function is estimated concave in
a firm’s own knowledge (see below), gaining scale economies in R&D or product development can
also not explain mergers in our model.
In the absence of any clear pattern, it seems reasonable to assume that mergers are random
and occur with an exogenously given probability. We pick this probability to match the average
observed merger rate. To avoid reaching a monopoly state, we model the expected number of
9In 2007 Chrysler and Daimler-Benz split. In 2008 Ford sold Jaguar and Rover brands to Tata Motors. These
splitsdonotfallintheperiodthatwestudy. Ourmodelcaneasilybemodifiedtoincorporatesplits. However,such
a modification will make an already hard estimation exercise even harder.
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mergers as a declining function of n (see the calibration of the merger probability in the Appendix)
and we impose a lower threshold on the number of active firms on competition policy grounds.
This is consistent with Klepper [2002a] and Klepper [2002b], who argues that the U.S. automobile
industry has settled into a stable oligopoly. We assume that the same will happen to the global
automobile industry. An alternative solution would be to introduce entry, but it would require
more radical changes in the empirical strategy. In practice, entry has not played an important role
in the evolution of the global market structure, although this could change with the development
of the Chinese and Indian automotive industries.
Even with this simple merger technology we need to specify the state variables for the newly
created firm and how each firm incorporates possible mergers in the evaluation of its future value.
First, we assume that when two firms merge, the knowledge of the new firm is the sum of the
individual knowledge stocks. Another possibility would be to allow for complementarities in the
knowledge stock or, at the other extreme, assume some overlap in knowledge and discount the sum.
All three assumptions are equally arbitrary, but we feel that simply adding the two knowledge
stocks is closest in spirit to the state transition function for knowledge used throughout. We
further assume that the unobserved quality of the vehicle produced by the merged firm is the
average of the unobserved qualities of the vehicles produced by the original firms.
Second, consider the situation where A and B are the only firms in the industry and their
respective states are (ω ,ξ ) and (ω ,ξ ).10 There is an exogenous probability p each period
A A B B m
that they will merge. Firm A will incorporate this information in the calculation of its value
functions as follow:
(cid:26)
V (ω ,ξ ,ω ,ξ ) = max π (ω ,ξ ,ω ,ξ )−cx (ω ,ξ ,ω ,ξ )
A A A B B A A A B B A A A B B
xA∈R+
(cid:104)
+ β p ζ (ω(cid:48) ,ξ(cid:48) ,ω(cid:48) ,ξ(cid:48) )EV (ω(cid:48) +ω(cid:48) ,(ξ(cid:48) +ξ(cid:48) )/2)
m A A A B B A A B A B
(cid:27)
(cid:105)
+(1−p )EV (ω(cid:48) ,ξ(cid:48) ,ω(cid:48) ,ξ(cid:48) ) , (9)
m A A A B B
where ζ (ω(cid:48) ,ξ(cid:48) ,ω(cid:48) ,ξ(cid:48) ) is the share of firm A in the total value of the merged firm. We assume
A A A B B
that it is simply the ratio of the stand alone value of A in the sum of the values of both firms in
absence of the merger:
EV(ω(cid:48) ,ξ(cid:48) ,ω(cid:48) ,ξ(cid:48) )
ζ (ω(cid:48) ,ξ(cid:48) ,ω(cid:48) ,ξ(cid:48) ) = A A B B .
A A A B B EV(ω(cid:48) ,ξ(cid:48) ,ω(cid:48) ,ξ(cid:48) )+EV(ω(cid:48) ,ξ(cid:48) ,ω(cid:48) ,ξ(cid:48) )
A A B B B B A A
The same idea extends to an industry with more firms, but the computations become more
involved—see the Appendix for details.
To summarize, in each period, the sequence of events is the following:
10The market size (m), which is the only industry-level state variable in our model, is implicit and we suppress it
for simplicity of notation.
8
1. Firms observe individual and industry states.
2. Pricing and investment decisions are made.
3. Profits and investment outcomes are realized.
4. Individual and industry states are updated before the mergers.
5. Mergers are drawn randomly (see Appendix for details).
6. The state variables of merged firms are adjusted and the industry states are updated accord-
ingly.
2.4 Markov Perfect Equilibrium
TheMarkovPerfectEquilibriumofthemodelconsistsofV(ω,ξ,s), π(ω,ξ,s), x(ω,ξ,s)andQ(s(cid:48),s)
such that:
1. V(ω,ξ,s) satisfies (17) and x(ω,ξ,s) is the optimal policy function;
2. π(ω,ξ,s) maximizes profits conditional on the state of the industry;
3. Q(s(cid:48),s) is the transition matrix that gives the probability of state s(cid:48) given the current state
s.
Estimating the parameters in the period profit function—demand and supply parameters—is
fairly standard. The one dynamic parameter in the model that we estimate, c the cost of R&D,
poses a greater challenge. There are at least two ways to proceed. The first is to compute the
Markov Perfect Equilibrium (MPE), as defined above, from a starting value of c and use maximum
likelihoodestimation,asinRust[1987]orHolmesandSchmitz[1995],tofittheobservedinvestment
decisions to the predictions from the model’s Euler equations. Having solved for the equilibrium
one can simulate the model and study the dynamics of interest. Benkard [2004] uses a similar
approach in his study of the market for wide-bodied commercial aircraft. The main disadvantage
of this approach is that the numerical solution for the MPE is computationally very intensive. This
remains a problem despite some recent innovations by Pakes and McGuire [2001], Doraszelski and
Judd [2004] and Weintraub et al. [2005].
The second is a two-step approach proposed by Bajari et al. [2007]. Their method allows for
the estimation of the policy and value functions and for the recovery of structural parameters of
the model without having to compute the MPE. They get around the problem of computing the
9
equilibrium by assuming that the data we observe represent an MPE.11 This assumption is not
completely innocuous. For example, in the auto industry firms often undergo structural changes
withadjustmentsspreadovermanyyears. Duringthistransitionperiod,firmsdonotbehaveasthey
would in equilibrium. A prime example would be the three-year recovery plan Renault initiated at
Nissan, when it took control of the troubled Japanese automaker. Similarly GM and Ford, having
lost a big chunk of their market share in recent years, are undergoing massive restructuring. Their
decisions during this restructuring phase are likely to be different from their decisions in a stable
equilibrium. Nevertheless, the assumption that firms always play their equilibrium strategy sounds
reasonable and, more importantly, does away with the need to compute the MPE.12
Given this assumption, the first step proposed by Bajari et al. [2007] is to estimate the state
transition probabilities and the equilibrium policy functions directly from the observed information
on investments and the evolution of the two state variables. Together with estimates of the period
profitfunction,thesecanbeusedtoobtainthevaluefunctionsbyforward-simulation. Inthesecond
step, the value function estimates from the first step are combined with equilibrium conditions of
themodeltoestimatethestructuralparameter(s). InSection4weelaborateonthesestepsinsome
detail and explain how we apply them to our model. First, we describe the data set.
3 Data
We choose our sample period to be 1982–2004. This period covers most of the consolidation that
has taken place in the industry over the last few decades. We limit the sample to the largest
thirteen firms (in terms of unit sales) that were active in 2004. The industry has seen significant
consolidationoverthesetwentythreeyearsandworkingbackto1982,thesethirteengroupsemerged
from twenty three initially independent firms. Throughout, we do not distinguish between full and
partial ownership ties, e.g. Nissan and Renault are treated as a single firm after they initiated an
alliance in 1998, even though Renault never obtained majority control. Table 2 lists all firms in the
initial and final year of the sample. In 2004, our sample accounted for slightly more than 95% of
global automobile sales. The remaining 5% fo sales were shared by a large number of small firms.
Since patenting activity of these small firms is negligible and other information on them is spotty,
11Alternative approaches that avoid computing the MPE at each iteration include Aguirregabiria and Mira [2007]
and Pakes et al. [2005]. We follow Bajari et al. [2007] because their method allows for continuous choice variable, as
we have in our model.
12Anotherimplicationofthisassumptionisthatonehastoruleoutthepossibilityofmultipleequilibriaorsimply
assume that multiple equilibria may exist but the firms only play one and the same equilibrium in all periods. For
a review of the problem of multiple equilibria in these models and its possible solutions see Section 6 in Doraszelski
and Pakes [2006].
10